Human Affective States Ontology for Sentiment Analysis

Tracking #: 1917-3130

This paper is currently under review
Authors: 
Rana Abaalkhail
Benjamin Guthier1
Abdulmotaleb El Saddik

Responsible editor: 
Lora Aroyo

Submission type: 
Ontology Description
Abstract: 
Social media provides a platform where users share an enormous amount of information about events, products,experiences and more. This information may contain user sentiments and feelings.Sentiment analysis helps monitor and analyze the opinions of users. An ontology has the ability to express the concepts shared, as well as their relationships, in a semantically rich representation. This strong feature enables an ontology to be applied in the area of sentiment analysis. In this paper, we propose the development of a Human Affective States Ontology, which we will refer to as HASO. We employ HASO to the problem of sentiment analysis. We argue that this ontology can compete with state of the art machine learning approaches to detect the sentiment contained in textual data. By using HASO, we classify the sentiment found in the SemEval-2017 dataset and compare our results with those obtained by the teams that participated in this task. The results of our work show the effectiveness of the proposed ontology (HASO) in capturing sentiment, especially when compared to machine learning approaches.
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